Finding Similar Movements in Positional Data Streams

Jens Haase; Ulf Brefeld

Finding Similar Movements in Positional Data Streams

Jens Haase
, Ulf Brefeld

Technische Universität Darmstadt

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

3 Zitate (Scopus)

Abstract

In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Near-neighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.

Originalsprache	Englisch
Titel	Machine Learning and Data Mining for Sports Analytics - MLSA 2013 : Proceedings
Redakteure/-innen	Davis Jesse, Jan Van Haaren, Albrecht Zimmermann
Seitenumfang	9
Erscheinungsort	Prag
Herausgeber (Verlag)	Sun Site Central Europe (RWTH Aachen University)
Erscheinungsdatum	2013
Seiten	49-57
Publikationsstatus	Erschienen - 2013
Extern publiziert	Ja
Veranstaltung	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013 - Prag, Tschechische Republik Dauer: 23.09.2013 → 27.09.2013 http://www.ecmlpkdd2013.org/

Fachgebiete und Schlagwörter

Informatik
Wirtschaftsinformatik

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Haase, J., & Brefeld, U. (2013). Finding Similar Movements in Positional Data Streams. in D. Jesse, J. Van Haaren, & A. Zimmermann (Hrsg.), Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings (S. 49-57). (CEUR Workshop Proceedings; Nr. 1969). Sun Site Central Europe (RWTH Aachen University). http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/mlsa13_submission_13.pdf

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author = "Jens Haase and Ulf Brefeld",

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booktitle = "Machine Learning and Data Mining for Sports Analytics - MLSA 2013",

address = "Germany",

note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013, ECMLPKDD 2013 ; Conference date: 23-09-2013 Through 27-09-2013",

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Haase, J & Brefeld, U 2013, Finding Similar Movements in Positional Data Streams. in D Jesse, J Van Haaren & A Zimmermann (Hrsg.), Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings. CEUR Workshop Proceedings, Nr. 1969, Sun Site Central Europe (RWTH Aachen University), Prag, S. 49-57, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013, Prag, Tschechische Republik, 23.09.13. <http://www.ecmlpkdd2013.org/wp-content/uploads/2013/09/mlsa13_submission_13.pdf>

Finding Similar Movements in Positional Data Streams. / Haase, Jens; Brefeld, Ulf.
Machine Learning and Data Mining for Sports Analytics - MLSA 2013: Proceedings. Hrsg. / Davis Jesse; Jan Van Haaren; Albrecht Zimmermann. Prag: Sun Site Central Europe (RWTH Aachen University), 2013. S. 49-57 (CEUR Workshop Proceedings; Nr. 1969).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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T1 - Finding Similar Movements in Positional Data Streams

AU - Haase, Jens

AU - Brefeld, Ulf

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N2 - In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Near-neighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.

AB - In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Near-neighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.

KW - Informatics

KW - Business informatics

UR - https://www.scopus.com/pages/publications/85007206303

M3 - Article in conference proceedings

T3 - CEUR Workshop Proceedings

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EP - 57

BT - Machine Learning and Data Mining for Sports Analytics - MLSA 2013

A2 - Jesse, Davis

A2 - Van Haaren, Jan

A2 - Zimmermann, Albrecht

PB - Sun Site Central Europe (RWTH Aachen University)

CY - Prag

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013

Y2 - 23 September 2013 through 27 September 2013

ER -

Finding Similar Movements in Positional Data Streams

Abstract

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